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Senior Data Scientist, Digital

Floor and Decor Outlets of America, Inc. Atlanta, GA

Atlanta, GA

Today

Full time Senior A/B Testing Artificial Intelligence

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Job Description

Floor & Decor Outlets of America, Inc. is hiring a Senior Data Scientist, Digital to build and run machine learning that improves the digital shopping experience.

Responsibilities

Build and improve ML models for product search and discovery using proprietary signals including customer behavior, store-level availability, and local demand.
Develop personalization and recommendation models for site and app, including “complete the project” recommendations and session-based models for anonymous and infrequent shoppers.
Apply LLMs and computer vision to enrich product attributes at catalog scale, with quality controls aligned to technical product specifications.
Partner with the Lead Data Scientist, AI/ML on conversational shopping and AI-assisted project guidance, owning retrieval quality and evaluation.
Monitor and manage quality, cost, latency, and safety for AI-assisted experiences.
Create durable ML assets that persist across eCommerce platform and vendor changes, including enriched product data, embeddings, and evaluation sets.
Lead quantitative evaluation of search and personalization vendors using Floor & Decor’s own data, recommending when platform configuration is sufficient versus when proprietary models provide defensible value.
Own production models on Databricks, provide documented outputs for Ecommerce and IT Engineering integration into site, app, and vendor platforms, and manage retraining and troubleshooting.
Help expand ML capabilities on Databricks as needs grow, and with the Lead Data Scientist, AI/ML define scalable ML engineering standards for a growing team.
Partner with Data Engineering, Product, and Engineering to define data and event tracking needed for training and measurement.
Design and analyze A/B tests for model-driven features; measure incremental impact on conversion, average order value, and engagement.
Ensure offline model improvements translate into online results; Ecommerce Analytics owns standard digital performance reporting.
Support the digital data science roadmap by sizing opportunities, assessing feasibility, and clarifying what models will and will not do.
Serve as technical lead for digital ML: guide work of the Data Scientist, Digital, raise technical quality via design and code reviews, and support recruiting and hiring.
Maintain clear documentation of methodologies, model assumptions, and limitations to support reproducibility and continuity.
Stay current on search, recommender systems, and applied generative AI and integrate relevant improvements into practice.
Follow Privacy Preferences, Responsible AI, and Security protocols, including guardrails for customer-facing generative AI.

Requirements

Bachelor’s degree in a related quantitative field (Computer Science, Statistics, Mathematics, Engineering, Economics, or similar). Master’s or PhD is a plus; relevant experience may substitute for education based on one calendar year of experience for one academic year of education.
5+ years of experience in data science or machine learning, or an equivalent combination of education and experience as determined by the company.
Experience building ML for live, customer-facing digital products such as search, recommendations, ranking, personalization, or ads in eCommerce, marketplaces, media, or comparable environments.
Proven ability to take multiple models into production and own them post-launch, including monitoring, troubleshooting, and retraining.
Depth in search and ranking or in recommendations and personalization, with enough breadth to contribute across both.
Applied experience with NLP, embeddings, and large language models, including retrieval and reranking and evaluating output quality.
Expert proficiency in Python and SQL, with production-quality coding practices.
Hands-on ML lifecycle experience with tooling such as automated training and scoring pipelines, deployment, and monitoring.
Rigor in model evaluation and online experimentation using A/B testing for digital products.
Comfort using AI-assisted development tools such as Claude Code, GitHub Copilot, or Cursor.
Ability to work as the roadmap and platforms evolve: define ambiguous problems, start simple, and add complexity when it delivers value.
Strong communication skills to explain model behavior, tradeoffs, and expected impact to product, engineering, and business leaders.

Technologies

Python, SQL, Natural language processing, Embeddings
Large language models, Retrieval, Reranking
A/B testing, Claude Code, GitHub Copilot, Cursor
Databricks, Azure, PySpark
CI/CD, MLflow, Model registry, Model serving
Feature stores
Bloomreach, Constructor, Algolia, Dynamic Yield, Salesforce Personalization, Salesforce Commerce Cloud
Retrieval-augmented generation (RAG), LLM-based agents

Benefits

Bonus opportunities & career advancement opportunities at every level
401k with company match
Employee Stock Purchase Plan
Referral Bonus Program
Medical, Dental, Vision, Life, and other Insurance Plans (subject to eligibility criteria)
Paid vacation and sick time for eligible associates
Paid holidays plus a personal holiday
Paid Volunteer Time Off that starts on Day 1

Preferred Qualifications

Experience helping establish an emerging ML function, including practices, tooling, and production patterns for a growing team.
Databricks on Azure, PySpark, and CI/CD for ML, including MLflow, model registry, model serving, and feature stores.
Experience extending, evaluating, or migrating commercial search/personalization platforms such as Bloomreach, Constructor, Algolia, Dynamic Yield, Salesforce Personalization, or Salesforce Commerce Cloud.
Semantic search, RAG, or computer vision applied to product discovery.
eCommerce or omnichannel retail experience, ideally in high-consideration, project-based categories (home improvement, furniture, home décor).
Experience with LLM-based agents or tool-using workflows.
Experience with clickstream and customer behavioral data at scale.
Experience with causal inference methods for measuring impact.
Experience mentoring other data scientists or serving as technical lead on a multi-person ML effort.

Working Conditions (Travel & Environment)

Travel may be required, including air and car travel (typically less than 10%).
Noise level in the work environment is typically quiet to moderate.

Location: Atlanta, GA (onsite)

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